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Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications de Olfa Nasraoui

Clustering Methods for Big Data Analytics: Techniques, Toolboxes and Applications: Unsupervised and Semi-Supervised Learning

Editat de Olfa Nasraoui, Chiheb-Eddine Ben N'Cir
en Limba Engleză Hardback – 8 noi 2018
This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation.

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Specificații

ISBN-13: 9783319978635
ISBN-10: 3319978632
Pagini: 292
Ilustrații: IX, 187 p. 63 illus., 31 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.46 kg
Ediția:1st ed. 2019
Editura: Springer International Publishing
Colecția Springer
Seria Unsupervised and Semi-Supervised Learning

Locul publicării:Cham, Switzerland

Cuprins

Introduction.- Clustering large scale data.- Clustering heterogeneous data.- Distributed clustering methods.- Clustering structured and unstructured data.- Clustering and unsupervised learning for deep learning.- Deep learning methods for clustering.- Clustering high speed cloud, grid, and streaming data.- Extension of partitioning, model based, density based, grid based, fuzzy and evolutionary clustering methods for big data analysis.- Large documents and textual data clustering.- Applications of big data clustering methods.- Clustering multimedia and multi-structured data.- Large-scale recommendation systems and social media systems.- Clustering multimedia and multi-structured data.- Real life applications of big data clustering.- Validation measures for big data clustering methods.- Conclusion.

Notă biografică

Olfa Nasraoui is the endowed Chair of e-commerce and the founding director of the Knowledge Discovery & Web Mining Lab at the University of Louisville, where she is also Professor in Computer Engineering & Computer Science. She received her Ph.D in Computer Engineering and Computer Science from the University of Missouri-Columbia in 1999. Her research interests are machine learning algorithms and systems with an emphasis on clustering algorithms, web mining, and recommender systems. She is the recipient of a National Science Foundation CAREER Award and a Best Paper Award for theoretical contributions In computational intelligence at the ANNIE conference.
Chiheb Eddine Ben N’cir received his Ph.D in Computer Science and Management from Higher Institute of Management, University of Tunis, in 2014. Currently, he is an Assistant Professor at the Higher School of Digital Economy (University of Manouba) since 2015 and member of LARODEC laboratory (University of Tunis). He is also a Big Data and Business Intelligence instructor at IBM North Africa and Middle East. His research interests concern unsupervised learning methods and data mining tools with a special emphasis on Big Data clustering, disjoint and non-disjoint partitioning, kernel methods, as well as many other related fields.

Textul de pe ultima copertă

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering, Cybersecurity applications such as insider threat detection, scalable distributed clustering methods for massive volumes of data; clustering Big Data Streams such as streams generated by the confluence of Internet of Things, digital and mobile health, human-robot interaction, and social networks; Spark-based Big Data clustering using Particle Swarm Optimization; and Tensor-based clustering for Web graphs, sensor streams, and social networks. The chapters in the book include a balanced coverage of big data clustering theory, methods, tools, frameworks, applications, representation, visualization, and clustering validation. 

Caracteristici

Includes the most recent and innovative advances in Big Data Clustering Describes recent tools, techniques, and frameworks for Big Data Analytics Introduces surveys, applications and case studies of Big Data clustering in Deep Learning, Blockchain, Cybersecurity, Data Streams, and Tensor graphs